Why Environmental Monitoring on Edge Devices Needs a Better Software Architecture
Why Environmental Monitoring on Edge Devices Needs a Better Software Architecture
The Problem
Autonomous environmental monitoring — deploying sensor nodes in the field to collect temperature, humidity, air quality, and other measurements over extended periods — is increasingly important for ecological research [1]. Raspberry Pi single-board computers have become a popular platform for this work, with applications ranging from autonomous acoustic ecosystem monitoring to remote bird observation and precision agriculture [2][3]: they are inexpensive, widely available, and run a full Linux operating system [4].
However, the software side of these deployments has not kept pace with the hardware. Most sensor-reading solutions available today suffer from a set of recurring problems that undermine their reliability and maintainability in the field.
Fragile Dependency Chains
A large number of sensor libraries for Raspberry Pi are written in Python and depend on large, complex package ecosystems. A single temperature sensor may require a virtual environment containing dozens of packages with native C extensions, totalling 50–100 MB of disk space. These dependency trees are brittle: a routine system update, a change in a transitive dependency, or a version incompatibility can silently break a node that has been running unattended for months.
On embedded devices with limited storage and no guarantee of network connectivity, this fragility is a serious liability. An environmental monitoring deployment cannot afford to fail because a package repository changed its metadata format or because a C extension failed to compile against a newer kernel header.
Excessive Resource Consumption
Interpreted-language sensor drivers carry a substantial overhead. Starting a Python interpreter, loading modules, and initialising a sensor library can take close to a second — before a single measurement is taken. For a node that reads multiple sensors on a regular schedule, this overhead compounds quickly: it increases power draw, reduces battery life, and limits the sampling rate that can be achieved.
Peak memory usage of 30–50 MB per sensor read is also problematic on devices with 512 MB or 1 GB of RAM, particularly when multiple sensors must be read concurrently.
Lack of a Uniform Interface
Different sensors use different communication protocols (GPIO bit-banging [5], I2C, 1-Wire [6], SPI), different libraries, and different output formats. Without a common contract between the system and its sensor drivers, every new sensor type requires bespoke integration work. This makes it difficult to build higher-level tooling — data aggregation, logging, remote management — that works across sensor types without special-casing each one.
The absence of a standard interface also makes it harder for researchers to share and reproduce monitoring configurations. A setup that works on one node may not transfer cleanly to another if the sensor scripts have different conventions for output format, error reporting, or configuration.
No Graceful Degradation
Sensor hardware is inherently unreliable in field conditions. Connections corrode, condensation forms on contacts, and communication protocols fail transiently. Many existing sensor solutions treat any read failure as fatal, or conversely, silently return stale or default data. Neither behaviour is acceptable for scientific data collection, where it is important to distinguish between a genuine measurement and a failure.
A robust sensor system must implement structured retry logic with backoff, validate data integrity (e.g. checksums), and report failures explicitly so that downstream systems can handle missing data appropriately.
The Case for Native, Modular Sensor Drivers
These problems point towards a specific architectural approach:
Native compiled drivers eliminate interpreter overhead and large dependency trees. A sensor driver compiled to a single binary with one or two shared-library dependencies occupies kilobytes rather than megabytes, starts in milliseconds rather than seconds, and has no dependency chain that can break independently of the system.
A standardised output contract — for example, a defined JSON schema that all drivers must produce — allows sensor orchestration, data logging, and monitoring tools to work uniformly across sensor types. New hardware support becomes a matter of writing a driver that conforms to the contract, not modifying the entire pipeline. Crucially, a standard contract also enables tool chaining: because every driver produces the same structured output, downstream tools — data validators, unit converters, aggregation pipelines, alerting systems — can be composed in sequence without knowledge of the upstream sensor. Each tool reads the contract, transforms or acts on it, and passes it along. This turns a collection of individual sensor drivers into a composable toolchain, following the same Unix philosophy of small programs connected by a common interface.
A modular package structure, where each sensor type is an independently installable and updatable package, allows nodes to carry only the software they need. It also means that a bug fix or improvement to one sensor driver can be deployed without touching the rest of the system.
Centralised orchestration that discovers installed drivers, runs them in parallel with appropriate timeouts, and decorates their output with node-level metadata (timestamps, device identifiers) removes duplicated logic from individual drivers and provides a single entry point for data collection.
Conclusion
Environmental monitoring at scale demands software that is as resilient as the hardware it runs on. The combination of native sensor drivers, a uniform data contract, modular packaging, and centralised orchestration addresses the core reliability, performance, and maintainability problems that plague current approaches. For autonomous field deployments — where nodes must operate unattended for months and every watt and megabyte matters — this architecture is not a luxury but a necessity.
References
-
Baker, E. Urban Research Station. https://ebaker.me.uk/urban-research-station
-
Jolles, J.W. (2021). Broad-scale applications of the Raspberry Pi: A review and guide for biologists. Methods in Ecology and Evolution, 12(9), 1562–1579. https://doi.org/10.1111/2041-210X.13652
-
Joice, A., Tufaique, T., Tazeen, H., Igathinathane, C. et al. (2025). Applications of Raspberry Pi for precision agriculture — A systematic review. Agriculture, 15(3), 227. https://doi.org/10.3390/agriculture15030227
-
Johnston, S.J., & Cox, S.J. (2017). The Raspberry Pi: A Technology Disrupter, and the Enabler of Dreams. Electronics, 6(3), 51. https://doi.org/10.3390/electronics6030051
-
Bruns, B. (2017). libgpiod: A new GPIO interface for Linux userspace. https://git.kernel.org/pub/scm/libs/libgpiod/libgpiod.git/about/
-
Kernel.org. (2024). w1_therm — Linux kernel 1-Wire thermometer driver documentation. https://docs.kernel.org/w1/slaves/w1_therm.html